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Exploiting the recent advancements in artificial intelligence, showcased byChatGPT and DALL-E, in real-world applications necessitates vast,domain-specific, and publicly accessible datasets. Unfortunately, the scarcityof such datasets poses a significant challenge for researchers aiming to applythese breakthroughs in engineering design. Synthetic datasets emerge as aviable alternative. However, practitioners are often uncertain about generatinghigh-quality datasets that accurately represent real-world data and aresuitable for the intended downstream applications. This study aims to fill thisknowledge gap by proposing comprehensive guidelines for generating, annotating,and validating synthetic datasets. The trade-offs and methods associated witheach of these aspects are elaborated upon. Further, the practical implicationsof these guidelines are illustrated through the creation of a turbo-compressorsdataset. The study underscores the importance of thoughtful sampling methods toensure the appropriate size, diversity, utility, and realism of a dataset. Italso highlights that design diversity does not equate to performance diversityor realism. By employing test sets that represent uniform, real, ortask-specific samples, the influence of sample size and sampling strategy isscrutinized. Overall, this paper offers valuable insights for researchersintending to create and publish synthetic datasets for engineering design,thereby paving the way for more effective applications of AI advancements inthe field. The code and data for the dataset and methods are made publiclyaccessible at https://github.com/cyrilpic/radcomp .

DATED: Guidelines for Creating Synthetic Datasets for Engineering Design Applications